Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ∼25%. Models and code are available at https://github.com/hujie-...
本发明提供基于SqueezeExcitationnetwork和ConNet网络的高效深度学习方法及系统,方法包括:获取蛋白质全局序列,蛋白质局部序列,以作为样本集,设定DeepNet深度框架的模型参数;划分得到训练集,验证集,设置DeepNet深度框架的模型架构,提取得到有效特征信息,组合所述蛋白质全局序列,所述蛋白质局部序列中的阴性样本与正样本,以送入...
SE Net (Squeeze-and-Excitation Networks) enhances the target feature focus. Bi FPN (Bi-Directional Feature Pyramid Network) performs multi-scale feature ... Li-Gang Wu,Zhe Zhang,Le Chen,... - 2024 43rd Chinese Control Conference (CCC) 被引量: 0 'Putting the squeeze' on the tight junc...
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks 来自 arXiv.org 喜欢 0 阅读量: 2472 作者:AG Roy,N Navab,C Wachinger 摘要: Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications. ...
We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three...
Squeeze-and-Excitation Networks. The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by... Jie,Shen,Samuel,... - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 被引量: 186发表: ...
Code and models are available at https://github.com/hujie-frank/SENet. 展开 关键词: Convolutional neural networks Squeeze-and-excitation Spatial pooling Base model 会议名称: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ...
Additionally, the proposed HP-CNN bring some advantages: 1) Squeeze-and-excitation block (SE-block) strengthens the representational power of networks by ... M Lou,L Chen,F Guo - International Conference on Multimedia & Expo 被引量: 0发表: 2019年 Multiple Pedestrians and Vehicles Tracking in...
The framework efficiently amalgamates the robustness and efficacy of three modern backbone networks, i.e., Multiscale CNN, Squeeze and Excitation (SE), and Transformer named MSTSENet. Initially, the multiscale convolution module integrates three branches of 3D convolution layers, each employing ...
Hence, this work proposes an architecture called FASENet, a 1D convolutional neural network-based two-stream fall detection and activity monitoring model using squeeze-and-excitation networks. By using pose keypoints as inputs for the model instead of raw video frames, it is able to use 1D ...